Harmonic Compensation of a Power-Hardware-in-the-Loop Based Emulator for Induction Machines
Bibliographic record
Abstract
Power-hardware-in-the-loop (PHIL)-based machine emulator systems use controlled power converters to mimic the behavior of an electric machine. In this article, a PHIL-based machine emulation system is proposed for grid-tied three-phase induction machines (IM). Typically, a switched voltage source inverter (VSI) is employed as an emulator converter in the motor emulation system. However, the VSI introduces various harmonics into the motor emulation system. These harmonics are mainly attributed to dead time, switching components, and control signals. These harmonics deteriorate motor emulation accuracy. Thus, it is important to investigate and compensate for emulator converter harmonics in motor emulation systems. As an important source of these harmonics is dead time, a detailed analysis of the dead time effect on motor emulation will be presented first. Subsequently, a novel artificial neural network (ANN)-based harmonic compensation technique is developed to ensure the mitigation of harmonics in the emulated motor currents. The proposed ANN-based intelligent harmonic compensator leads to the improvement of motor emulation accuracy. Experimental results are obtained from the emulator system and a 5 hp squirrel cage induction motor to validate the proposed emulator with harmonic compensation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".